For the second issue, current methods believe that they can well handle the CoSOD task by only using intra-image saliency and inter-image consistency. However, the inter-image consistency only provides positive relations while lacking negative relations between different objects. Training the model only using positive pairs cannot provide enough information for learning a discriminative representation. When facing complex real-world scenarios, the model cannot distinguish true common objects from noisy objects. As can be seen in Fig.REF , two representative existing methods ICNet [1]} and GICD [2]} fail to distinguish co-salient objects because they lack consideration of inter-image separability. Inspired by contrastive learning [3]}, we propose a novel contrastive loss for CoSOD to model the inter-image separability. We not only regard the co-salient regions in an image group as positive relations but also utilize the non-co-salient regions to build negative relations. Through contrastive loss, the true common objects should be similar to each other and dissimilar to other noisy objects in the embedding space. Finally, we can learn a discriminative representation to get better performance.
